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We present the development of a Web‐based remote‐monitoring system as an attractive alternative to the manual task of security surveillance. The system has built‐in human motion analysis that can alert the security personnel when suspicious activities are detected at the monitored sites. In this paper, our focus is on the architectural design of such a system. We discuss the relative strengths and weaknesses of three different architectural approaches: centralized, distributed and hybrid, and conclude that a hybrid approach is most appropriate. Our Web‐based system has been implemented for monitoring elevators (lifts) within a university environment. Objective and subjective tests have validated the effectiveness of our system. The Web‐based implementation enhances ease of operation and allows users to connect to the system via a Web browser.

Two common methods in community settings of assessing program fidelity, a critical implementation component for program effectiveness, are video and audio recordings of sessions. This paper aims to examine how these two methods compared when used for a home-based behavioral parenting-training model (SafeCare®).

Twenty-five SafeCare video-recorded sessions between home visitors and parents were scored by trained raters either using the video or audio-only portions of recordings. Sessions were coded using fidelity checklists, with items (n = 33) classified as one of two fidelity aspects, content [delivery of program components (n = 15)], or process [communication and rapport building (n = 11)]. Seven items were considered to overlap between constructs. Items were coded as having been done or not done appropriately. Coders rated items as “technological limitation” when scoring methods hindered coding. Analyses compared percent agreement and disagreement between audio and video coders.

Overall agreement between coders was 72.12%. Levels of agreement were higher for content items (M = 80.89%, SD = 19.68) than process items (58.54%, SD = 34.41). Disagreements due to technology limitations among audio coders were noted among 15 items; particularly, higher levels of disagreement were seen among process items (42.42%) than content items (9.64%).

Compared to video, fidelity monitoring via audio recordings was associated with some loss of process-related fidelity. However, audio recordings could be sufficient with supplements such as participant surveys, to better capture process items. Research should also examine how content and process fidelity relate to changes in family behavior to further inform optimal fidelity monitoring methods for program use.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research project on the application of computer vision within a municipal police department is described. The paper aims to discuss these issues.

Following the demystification of computer vision technology, its potential for police agencies is developed within a focus on computer vision as a solution for two common surveillance camera tasks (live monitoring of multiple surveillance cameras and summarizing archived video files). Three unaddressed research questions (can specialized computer vision applications for law enforcement be developed at this time, how will computer vision be utilized within existing public safety camera monitoring rooms, and what are the system-wide impacts of a computer vision capability on local criminal justice systems) are considered.

Despite computer vision becoming accessible to law enforcement agencies the impact of computer vision has not been discussed or adequately researched. There is little knowledge of computer vision or its potential in the field.

This paper introduces and discusses computer vision from a law enforcement perspective and will be valuable to police personnel tasked with monitoring large camera networks and considering computer vision as a system upgrade.

The purpose of this paper is to examine the practical and legal complexities associated with tele-homeworking arrangements in light of the recent COVID-19 pandemic. In particular, the study focusses on organisational practices and outcomes relating to the monitoring and surveillance of employees. Drawing on relevant UK legislation and illustrative case law examples, the study demonstrates the challenges and legal implications associated with tele-homeworking.

This study is based on a review of the literature and an examination of the EU and UK laws applicable to various employer and employee concerns that stem from tele-homeworking.

Tele-homeworking can be advantageous to both employers and employees, however, there are a number of growing concerns surrounding the monitoring of such workers. Developing technologies can act as a catalyst for legal disputes and the advances in workforce monitoring and surveillance reveal the complex challenges faced by both employers and employees. The indiscriminate monitoring of staff can result in claims of violations to the privacy rights of workers, breach of contract and discrimination claims. Several policy implications associated with monitoring tele-homeworkers surface from the analysis, including the need to ensure that any proposed surveillance is legitimate, proportionate and transparent.

The paper is beneficial in providing legal insights into the topical and continuing complexities associated with the monitoring of tele-homeworkers. The exogenous shock of COVID-19 has demanded the reorganisation of work. The extensive and developing capabilities that employers have at their disposal to engage in employee monitoring, give rise to a greater possibility of legal challenges by workers. The study serves to draw attention to various surveillance concerns and highlights the importance of employers undertaking an evaluation of their monitoring practices and complying with the legal framework.

Organizations and their actors are increasingly using video surveillance to monitor organizational members, employees, clients, and customers. The use of such technologies in workplaces creates a virtual panopticon and increases uncertainty for those under surveillance. Video surveillance in organizations poses several concerns for the privacy of individuals and creates a security-privacy dilemma for organizations to address. The purpose of this paper is to offer a decision-making model that ties in ethical considerations of access, equality, and transparency at four stages of video surveillance use in organizations: deployment of cameras and equipment, capturing footage, processing and storing data, and editing and sharing video footage. At each stage, organizational actors should clearly identify the purpose for video surveillance, adopt a minimum capability necessary to achieve their goals, and communicate decisions made and actions taken that involve video surveillance in order to reduce uncertainty and address privacy concerns of those being surveilled.

The paper proposes a normative model for ethical video surveillance organizational decision making based on a review of relevant literature and recent events.

The paper provides several implications for the future of dealing with security-privacy dilemmas in organizations and offers structured considerations for corporation leaders and decision makers.

The paper includes implications for organizations to approach video surveillance with ethical considerations for stakeholder privacy while balancing security demands.

This paper offers a framework for decision-makers that also offers opportunities for further research around the concept of ethics in organizational video surveillance.

The use of the Internet for Web‐based teaching and learning is fast becoming a reality. However, since it is difficult to verify the identity of the student through a simple user ID and password system on the client side, performance evaluation through test and examination through the Internet is still in its infancy. To overcome this main hurdle, a system has been designed and developed where a camera at the client computer is used to capture the student’s face and posture at random intervals during the test. The captured images are stored at the server and can be used to verify the identity of the person taking the test if the need arises. The system developed has been successfully used in a randomized multiple choice test in a course on analog and digital signals involving 450 students.

More and more organizations have resorted to the employment of monitoring software to keep track of employees’ everyday performance and task completion. The current paper aims to outline the capabilities, pros and cons of monitoring for employees. Several recommendations for Human Resources (HR) professionals are outlined to inform best practice.

This paper summarizes recent literature and trends on electronic monitoring aimed at remote workers, focusing specifically on trends observed in the UK and the USA.

The number of pros and cons, as well as the resulting recommendations for HR professionals, outline how technology may aid – but also undermine – performance.

The summary of capabilities, pros and cons represents a snapshot of current monitoring practices. The recommendations will give readers an overview of all the aspects and factors that ought to be considered when monitoring software and related tools are selected.

The purpose of this paper is to improve an image defogging algorithm based on a dark channel prior and use this method to clear the foggy image on the Advanced RISC (Reduced Instruction Set Computing) Machines (ARM) platform.

The divided strategy of the foggy image was proposed through the estimation of the brightness and transmission thresholds. The two regions of the foggy image were processed using two transmissions. Many foggy images were tested using the improved method.

The finding resulting from this study is that a divided strategy has been proposed to use the image defogging. Compared with the existing methods, the running time of the improved method is less on the ARM platform.

Image enhancement is an important technology of digital images, and the quality of images plays a key role in the video monitoring and the intelligent transportation system.

This paper presented an improved image defogging method using the divided strategy and a substantial number of experimental results was provided to demonstrate this method.

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.

This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.

The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.

This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.